



'''simple docstring'''






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase:   Tuple					=		1_6
lowerCAmelCase:   Optional[int]					=		3_2
def  lowerCamelCase__			(		_A					,     _A = 16							):
  a		:   Optional[Any]	  =	AutoTokenizer.from_pretrained('bert-base-cased'							)
  a		:   Union[str, Any]	  =	load_dataset('glue'					,     'mrpc'							)

  def tokenize_function(_A							):
    # max_length=None => use the model max length (it's actually the default)
    a		:   Optional[Any]	  =	tokenizer(examples['sentence1']					,     examples['sentence2']					,     truncation=_A					,     max_length=_A							)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    a		:   List[str]	  =	datasets.map(
        _A					,     batched=_A					,     remove_columns=['idx', 'sentence1', 'sentence2']					,     )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  a		:   Union[str, Any]	  =	tokenized_datasets.rename_column('label'					,     'labels'							)

  def collate_fn(_A							):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a		:   Union[str, Any]	  =	128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      a		:   Dict	  =	16
    elif accelerator.mixed_precision != "no":
      a		:   List[str]	  =	8
    else:
      a		:   int	  =	None

    return tokenizer.pad(
        _A					,     padding='longest'					,     max_length=_A					,     pad_to_multiple_of=_A					,     return_tensors='pt'					,     )

  # Instantiate dataloaders.
  a		:   Optional[int]	  =	DataLoader(
      tokenized_datasets['train']					,     shuffle=_A					,     collate_fn=_A					,     batch_size=_A							)
  a		:   Dict	  =	DataLoader(
      tokenized_datasets['validation']					,     shuffle=_A					,     collate_fn=_A					,     batch_size=_A							)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     lowerCAmelCase:   Union[str, Any]					=		mocked_dataloaders  # noqa: F811
def  lowerCamelCase__			(		_A					,     _A							):
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS'					,     _A							) == "1":
    a		:   Tuple	  =	2
  # New Code #
  a		:   Optional[int]	  =	int(args.gradient_accumulation_steps							)
  # Initialize accelerator
  a		:   List[Any]	  =	Accelerator(
      cpu=args.cpu					,     mixed_precision=args.mixed_precision					,     gradient_accumulation_steps=_A							)
  if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
    raise NotImplementedError(
        'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'							)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a		:   List[str]	  =	config['lr']
  a		:   List[str]	  =	int(config['num_epochs']							)
  a		:   List[str]	  =	int(config['seed']							)
  a		:   List[str]	  =	int(config['batch_size']							)

  a		:   str	  =	evaluate.load('glue'					,     'mrpc'							)

  set_seed(_A							)
  a  ,      a		:   List[str]	  =	get_dataloaders(_A					,     _A							)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a		:   Optional[Any]	  =	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,     return_dict=_A							)

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  a		:   Optional[int]	  =	model.to(accelerator.device							)

  # Instantiate optimizer
  a		:   Union[str, Any]	  =	AdamW(params=model.parameters()					,     lr=_A							)

  # Instantiate scheduler
  a		:   List[Any]	  =	get_linear_schedule_with_warmup(
      optimizer=_A					,     num_warmup_steps=100					,     num_training_steps=(len(_A							) * num_epochs)					,     )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  a  ,      a  ,      a  ,      a  ,      a		:   Dict	  =	accelerator.prepare(
      _A					,     _A					,     _A					,     _A					,     _A							)

  # Now we train the model
  for epoch in range(_A							):
    model.train()
    for step, batch in enumerate(_A							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      # New code #
      # We use the new `accumulate` context manager to perform gradient accumulation
      # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
      with accelerator.accumulate(_A							):
        a		:   Dict	  =	model(**_A							)
        a		:   List[str]	  =	output.loss
        accelerator.backward(_A							)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    for step, batch in enumerate(_A							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      with torch.no_grad():
        a		:   Optional[Any]	  =	model(**_A							)
      a		:   Dict	  =	outputs.logits.argmax(dim=-1							)
      a  ,      a		:   List[Any]	  =	accelerator.gather_for_metrics((predictions, batch['labels'])							)
      metric.add_batch(
          predictions=_A					,     references=_A					,     )

    a		:   int	  =	metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:"""					,     _A							)
def  lowerCamelCase__			(		):
  a		:   Optional[int]	  =	argparse.ArgumentParser(description='Simple example of training script.'							)
  parser.add_argument(
      '--mixed_precision'					,     type=_A					,     default=_A					,     choices=['no', 'fp16', 'bf16', 'fp8']					,     help='Whether to use mixed precision. Choose'
      'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
      'and an Nvidia Ampere GPU.'					,     )
  # New Code #
  parser.add_argument(
      '--gradient_accumulation_steps'					,     type=_A					,     default=1					,     help='The number of minibatches to be ran before gradients are accumulated.'					,     )
  parser.add_argument('--cpu'					,     action='store_true'					,     help='If passed, will train on the CPU.'							)
  a		:   int	  =	parser.parse_args()
  a		:   Any	  =	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_A					,     _A							)


if __name__ == "__main__":
     main()




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase:   List[str]					=		{
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   str					=		[
         'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FalconForCausalLM',
         'FalconModel',
         'FalconPreTrainedModel',
         'FalconForSequenceClassification',
         'FalconForTokenClassification',
         'FalconForQuestionAnswering',
     ]


if TYPE_CHECKING:
     from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_falcon import (
              FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
              FalconForCausalLM,
              FalconForQuestionAnswering,
              FalconForSequenceClassification,
              FalconForTokenClassification,
              FalconModel,
              FalconPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase:   Optional[int]					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)